A Claim Pairing Approach to Measuring Superimposed Inflation

There are many ways of measuring superimposed inflation. In this article, Aaron Cutter of Finity explains how to measure superimposed inflation by pairing claims that ‘look-a-like’ – a technique adapted from home price inflation techniques.

Many factors influence the amounts that are ultimately paid to claimants as settlement of bodily injury claims. Injury severity and personal circumstances are two such factors. However, because there can be a spectrum of characteristics within each factor, plus confounding effects of other factors, traditional techniques on aggregate payment experience to understand superimposed inflation have inherent shortcomings. Also, there is some ambiguity in what is being measured by and hence how best to measure superimposed inflation.

Observing settlement amounts associated with the exact same claim from two different settlement periods would provide the purest measure of superimposed inflation (once underlying economic inflation is allowed for). Clearly that is not possible. However, finding and pairing two or more claims that are almost identical is feasible.

What is a claim pairing index?

Akin to the methodology used in compiling house price indices, we have developed a claim pairing approach to measuring superimposed inflation. The concept is to:

Find ‘pairs’ of similar claims which have settled in different time periods

Calculate the inflation between these claim pairs. This contributes to the inflation index for the period between their settlement dates.

Application to NSW CTP Claims

We have applied this approach in the context of NSW CTP claims.

The pairing approach:

Compares every possible combination of claims, and generates a similarity score based on the claim characteristics. Characteristics have different contributions to the score, and are based on statistical modelling of the drivers of cost. For example, the combination of injury codes has the highest weight. Other factors such as the occupation and liability status of the claimant, whilst still important, are given lower weightings.

Finds the set of claim pairs which optimises the similarity scores, subject to the constraint that a claim can contribute to a period’s inflation only once (i.e. any one claim can be matched to at most one claim which settled earlier, and one claim settled after).

Each claim pairing implies a level of inflation between the settlement dates of the claims. We combine these inflation points into an inflation index, using a bootstrapping process which is analogous to but a bit more complicated than calculating forward rates from spot curves. The construction of an inflation index in this way is more complex because:

There are multiple claim pairs contributing to each periods’ inflation, and the size of these claims can vary significantly. Care needs to be taken to allow appropriate weighting to each claim pair’s contribution to the inflation index

There are pairs which cross multiple periods. Noting that inflation is not uniform across the entire period, care needs to be taken to ensure this is dealt with correctly

Once the inflation index is constructed and wage inflation backed out we can form a view of superimposed inflation. The chart below illustrates our results:

Superimposed Inflation – NSW CTP Claims

Our index estimates:

Superimposed inflation was very high up until 2007/08. This is consistent with generally accepted views that claims inflation in NSW CTP portfolios was high over this period due to the Economic Loss and Care heads of damage.

Between 2008/09 and 2013/14, superimposed inflation was lower but still material, varying between 1% and 8% (average of 4% over these six years).

For 2014/15 and 2015/16, our index estimates that superimposed inflation has been negligible or even negative at -3% to -1%.

Our approach lends itself to examination of the types of claims contributing to superimposed inflation. We have examined superimposed inflation within each maximum injury severity group (excluding workers compensation claims).

The following graph shows the superimposed inflation, as measured using our pairing approach, from 2008/09 for each injury severity group (maximum AIS).

We observe that:

The high superimposed inflation in 2008/09 to 2012/13 is driven by sustained high inflation for Severity 0&1 claims, in conjunction with some high contributions from the Severity 2 and Severity 3 claims.

The low superimposed inflation in the two most recent years is driven by low inflation for Severity 0&1 claims, and strong negative inflation for Severity 3+ claims.

Note that the approach we have adopted specifically excludes any impact on total costs associated with more claims notified, accepted and ultimately paid.

The flexibility of the approach lends itself well to a number of applications, including setting pricing and reserving assumptions although, the interpretation of results needs to be conscious of the application.

Concluding remarks

We have illustrated how a claim pairing index approach can be applied in the context of NSW CTP claims. This is one example of how using advanced analytical techniques can enrich and draw new insights from your data that aren’t otherwise apparent.